Beyond the Rhetoric of Essentiality: Canada’s Neoliberal Migrant Worker Policy during the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction For decades, Canada has relied on migrants whose legal status in the country is temporary and/or conditional to fill critical jobs that are considered ‘dirty, dangerous and difficult’ and therefore shunned by local workers (Hennebry and Preibisch, 2012: e22). Between 1998 and 2017, the number of migrants in Canada who held work permits that became effective in a given year more than doubled from just under 111,600 to over 302,600 (Immigration, Refugees and Citizenship Canada, 2019). These temporary foreign workers play a particularly substantial role in the country’s food supply chain. In 2017, they made up 27 per cent of the workforce in the crop production industry, 5.6 per cent in animal production and aquaculture, and 3.4 per cent in food manufacturing. In the same year, they also accounted for nearly 10 per cent of all workers employed in private households as caregivers, housekeepers and so forth (Liu, 2020). Many of these temporary foreign workers lack a legal pathway to becoming permanent residents. Instead, they are often issued closed work permits that effectively tie them to specific employers if they wish to remain in or return to Canada (Preibisch, 2010). Aside from migrants entering Canada through the various temporary worker programs, those who seek asylum in the country as well as those who are undocumented are also key to Canada’s essential industries. For example, at the start of the COVID-19 pandemic, many orderlies working in long-term care facilities in the province of Quebec were current or refused asylum seekers, including upwards of 5,000 Haitians that arrived after 2017, seeking to escape anti-immigration policies in the United States (Stevenson and Shingler, 2020). On the other hand, undocumented migrants, who numbered approximately half a million in Canada, are known to work predominantly in the construction, manufacturing, hospitality and domestic service sectors (Magalhaes et al, 2010). Like that of their temporary foreign worker counterparts, asylum seekers’ and undocumented migrants’ legal status is highly precarious, as their presence in Canada is typically marked by uncertainty, illegality and heightened deportability (Goldring et al, 2009).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.037 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".